AI Brings Institutional Market Data Tools to More Retail Investors

AI is making market information easier for individual investors to gather and interpret, narrowing a longstanding access gap with professional trading firms. But the benefits depend on reliable underlying data and safeguards that match what the tools can actually do. That’s the view of Brandon Tepper, Nasdaq’s Senior Vice President and Global Head of Data, who describes a shift in how financial information is delivered and used.

“AI is really helping investors gather data in a more easy to use fashion to help tell stories from that data so that they can make informed investing decisions,” Tepper told Traders Magazine.

Access is widening, but data quality still sets the limit

Nasdaq is seeing new clients, applications and use cases as financial institutions, technology providers and individual investors look for ways to draw more value from existing datasets. The change spans retail investors, institutional traders and quantitative funds. In Tepper’s account, AI is becoming part of the way market information is organized and consumed across those groups.

The tools can help users make sense of large volumes of information, but their output is only as dependable as the data they receive. “In order to have good AI, as you know, you really have to have clean, trusted, and accurate data,” Tepper said. He also cautioned that AI systems can be wrong and that predictions depend heavily on the quality of their inputs: “The value of the underlying data is the basis for good AI, and there are risks of using incorrect data.”

That makes data provenance and accuracy central to the usefulness of AI-assisted analysis. A tool may present information in a more accessible form, but that presentation doesn’t repair errors in the source material. Users also need to understand an application’s limits, and providers need controls and security measures appropriate to its function.

Nasdaq operates 18 exchanges globally, Tepper said. Its exchange data can serve as a foundation for analysis, with complementary and alternative datasets added to provide further information. This layered approach matters because different data sources can answer different questions; combining them can broaden an analysis, while also making it important to know what each source represents.

Retail tools draw on more advanced data

Tepper said the gap between individual and professional investors is getting narrower. Some information once accessed through expensive financial terminals or proprietary datasets is becoming easier to reach through technology platforms. The change is less about a single tool than about giving individuals more ways to work with market intelligence that was previously harder to obtain.

Nasdaq’s expanded relationship with Robinhood is one example. The companies have worked together for more than a decade, starting with equity market data and extending to more sophisticated datasets, including Nasdaq TotalView. Nasdaq has more recently begun supplying alternative datasets through Robinhood’s Agents ecosystem, where users can ask AI agents investment-related questions using market intelligence.

“We’re not just streaming live data, we’re helping investors utilize it in a more agentic way,” Tepper said. He sees a possible next step in which individuals could set algorithmic strategies tied to predefined market conditions. That capability would move the tools beyond answering questions toward taking action based on user-selected rules, increasing the importance of clear limits and risk controls.

Tepper also described a growing group of sophisticated retail traders as “pro-tail” investors, who seek more advanced market information. Greater access may help these users work with richer data, but it doesn’t remove uncertainty from trading or ensure that an AI-generated interpretation is sound.

He pointed to Robinhood’s safeguards for its agent-based functionality: users must establish and separately fund an account, and access to information outside the platform is restricted. Tepper said the data is not used to train the underlying AI models. “If you have the right rails and the right security and risk profile, and understand what it can and can’t do, it’s going to just get better and easier to use,” he said.

Preparing data for AI and longer trading hours

Nasdaq has been preparing its datasets for AI-driven applications for several years, Tepper said. That work includes AI-ready data delivery capabilities and support for Model Context Protocol (MCP). He said AI now comes up in conversations with existing and prospective clients, startups and individual investors, reflecting demand across the market rather than within one customer segment alone.

The other shift Tepper highlighted is the expansion of trading hours. Nasdaq is diversifying across geographies and product categories, with more than 250 products available through Nasdaq Data Link. It is also preparing for demand for market information beyond traditional trading hours as the industry moves toward 23-hour, five-day trading, and potentially continuous markets.

Tepper said Nasdaq has been testing new data feeds in preparation for a December 6 launch of 23/5 trading. Longer sessions would increase the periods in which investors may want market information, putting attention on the availability and delivery of data outside the familiar daily schedule. Tepper tied the change to consumer expectations of being able to access services at almost any hour, asking, “Why not have the ability to invest in your favorite company at all hours of the day?”

For data providers and platform users, the two developments reinforce each other: more available trading time creates demand for timely information, while AI tools offer new ways to interpret it. Tepper sees room for both retail and institutional investors to use these capabilities as trading hours expand, with risk controls remaining part of the picture. The practical test will be whether platforms can make broader access useful while keeping data quality, security and the limits of automated activity clear.